Update README.md

README.md

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-license: etalab-2.0
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+---
+license: etalab-2.0
+pipeline_tag: image-segmentation
+tags:
+- semantic segmentation
+- pytorch
+- landcover
+library_name: pytorch
+---
+
+We introduce MAESTRO, a tailored adaptation of the Masked Autoencoder (MAE) framework that effectively orchestrates the use of multimodal, multitemporal, and multispectral Earth Observation (EO) data. Evaluated on four EO datasets, MAESTRO sets a new state-of-the-art on tasks that strongly rely on multitemporal dynamics, while remaining highly competitive on tasks dominated by a single monotemporal modality.
+
+Our contributions are as follows:
+
+Extensive benchmarking of multimodal and multitemporal SSL: Impact evaluation of various fusion strategies for multimodal and multitemporal SSL.
+Patch-group-wise normalization: Novel normalization scheme that normalizes reconstruction targets patch-wise within groups of highly correlated spectral bands.
+MAESTRO: Novel adaptation of the MAE that combines optimized fusion strategies with our tailored patch-group-wise normalization.
+
+<div style="position: relative; text-align: center;">
+    <img src="./media/Maestro_Overview.png" alt="Classes distribution." style="width: 100%; display: block; margin: 0 auto;"/>
+</div>
+
+
+
+
+